SnapCatch: Automatic Detection of Covert Timing Channels Using Image Processing and Machine Learning

نویسندگان

چکیده

With the rapid growth of data exfiltration carried out by cyber attacks, Covert Timing Channels (CTC) have become an imminent network security risk that continues to grow in both sophistication and utilization. These types channels utilize inter-arrival times steal sensitive from targeted networks. CTC detection relies increasingly on machine learning techniques, which statistical-based metrics separate malicious (covert) traffic flows legitimate (overt) ones. However, given efforts attacks evade growing column CTC, covert needs improve performance precision detect prevent CTCs mitigate reduction quality service caused process. In this article, we present innovative image-based solution for fully automated localization. Our approach is based observation generate can be converted colored images. Leveraging observation, our designed automatically locate part (i.e., set packets) within a flow. By locating parts flows, reduces drop blocking entire are detected. We first convert into images, then extract features traffic. train classifier using these large overt This demonstrates remarkable achieving accuracy 95.83% cautious 97.83% 8 bit messages, way beyond what popular solutions achieve.

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ژورنال

عنوان ژورنال: IEEE Access

سال: 2021

ISSN: ['2169-3536']

DOI: https://doi.org/10.1109/access.2020.3046234